A Data Management Plan (DMP) serves as a guide for all types of research and data collection. It assists the investigator in planning the data collection and organisation, and also helps in paying attention to certain regulations, law, licenses, ethical guidelines, etc.
Most data management plans have several sections like: Project description, Planning, Costs, Method of data collection and/or re-use, Data assets (description and documentation), Storage, Access (terms and conditions for sharing during and after research, which influences e.g. respondent consent forms, usage licenses, consortium agreements), Archiving, Ethical & Legal framework, Support.
If a research project involves multiple partners from different organisations it may be necessary to draw up a specific separate Consortium Agreement. In a DMP all partners that are involved in the collection, handling etc. of the data should be mentioned.
The DMP is a working document and needs to be updated over the course of the project whenever significant changes arise, such as (but not limited to):
The DMP should be updated in time with the periodic evaluation/assessment of the project, preferably also in between evaluations. If there are no other periodic reviews foreseen within the grant agreement, then such an update needs to be made in time for the funder's final review at the latest. Furthermore, the consortium can define a timetable for review in the DMP itself. Basically, the DMP should be updated whenever something meaningful changes or a significant milestone is reached.
There are many forms and formats for Data Management Plans and many funders have their own template. You can find information and support on creating your Data Management Plan here:
Data can be many things and in some cases metadata (data about data) can also be data to somebody else. For practical purposes data can be defined as follows:
“The data, records, files or other evidence, irrespective of their content or form (e.g. in print, digital, physical or other forms), that comprise research observations, findings or outcomes, including primary materials and analysed data.” (Monash University Research Data Management Procedures: HDR candidates).
Research data come in different forms: measurement data, pictures, geographical data, models, chromatograms, surveys. Sometimes even metadata (data about data) can be another researcher's data. It is also common practice to make the distinction between Qualitative and Quantitative research in connection with this. Quantitative data are data that can be quantified and verified, and are amenable to statistical manipulation. Quantitative data define whereas qualitative data describe. Qualitative data approximate or characterize but do not measure the attributes, characteristics, properties, etc., of a thing or phenomenon.
For practical purposes, when we are talking about (research) data we are specifically talking about digital data. However, in your Data Management Plan, you should also consider how you will store and archive physical data, such as paper questionnaires, samples and models, if any.
There are many types of digital data that may be used at different phases during research. Depending on the type data it may be saved or used for different purposes. Examples are:
|Data Stage||Dataset description||Type of data||Format|
|Raw data||Interviews||Audio files||MP3|
|Spectographic analysis||Text files||CSV|
|Processed data||Transcription of interviews||Word files||Docx|
|Data spreadsheet||SPSS files||SAV|
|Analysed data||Regression graphic||Photoshop files||PSD|
|Data table||Word file||Docx|